---
title: "tsfeatures (R package): papers citing this CRAN package"
description: "6 papers in the tracked journals link to tsfeatures (R package) in their Crossref records, the first in 2019; most of them (2) in 2022."
url: https://scholar4ai.com/code/cran-tsfeatures
markdown_url: https://scholar4ai.com/code/cran-tsfeatures.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "6 papers in the tracked journals link to tsfeatures (R package) in their Crossref records, the first in 2019; most of them (2) in 2022. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# tsfeatures (R package): papers linking to it

Research papers in the 480 tracked journals that link to tsfeatures (R package) (https://CRAN.R-project.org/package=tsfeatures), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- CRAN
- https://CRAN.R-project.org/package=tsfeatures

All repositories: https://scholar4ai.com/code.md

## Key findings

- 6 papers in the tracked journals link to tsfeatures (R package) in their Crossref records, the first in 2019; most of them (2) in 2022.
- By category: AI (3), Operations Research (2) and Economics (1); the journals with the most are Data Mining and Knowledge Discovery (3), European Journal of Operational Research (2) and International Journal of Forecasting (1).
- 6 of the papers build on one another: a later paper cites an earlier one that also links tsfeatures (R package).

## By year

*Papers linking to tsfeatures (R package) by publication year*

| Year | Papers |
|---|---:|
| 2019 | 2 |
| 2020 | 1 |
| 2021 | 1 |
| 2022 | 2 |

## Journals

*Journals whose papers link to tsfeatures (R package) most*

| Journal | Papers |
|---|---:|
| [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | 3 |
| [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | 2 |
| [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [catch22: CAnonical Time-series CHaracteristics](https://doi.org/10.1007/s10618-019-00647-x) (Data Mining and Knowledge Discovery, 2019)
  - [Forecast evaluation for data scientists: common pitfalls and best practices](https://doi.org/10.1007/s10618-022-00894-5) (Data Mining and Knowledge Discovery, 2022) — cites the paper above
- [FFORMA: Feature-based forecast model averaging](https://doi.org/10.1016/j.ijforecast.2019.02.011) (International Journal of Forecasting, 2019)
  - [Retail sales forecasting with meta-learning](https://doi.org/10.1016/j.ejor.2020.05.038) (European Journal of Operational Research, 2020) — cites the paper above
    - [Forecast with forecasts: Diversity matters](https://doi.org/10.1016/j.ejor.2021.10.024) (European Journal of Operational Research, 2021) — cites the paper above
  - [Novel features for time series analysis: a complex networks approach](https://doi.org/10.1007/s10618-022-00826-3) (Data Mining and Knowledge Discovery, 2022) — cites the paper above

## Papers

*Papers linking to tsfeatures (R package), most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2019-08-09 | [catch22: CAnonical Time-series CHaracteristics](https://doi.org/10.1007/s10618-019-00647-x) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Carl H Lubba, Sarab S. Sethi, Philip Knaute et al. | 442 | cites it |
| 2019-09-27 | [FFORMA: Feature-based forecast model averaging](https://doi.org/10.1016/j.ijforecast.2019.02.011) | [International Journal of Forecasting](https://scholar4ai.com/journals/international-journal-of-forecasting.md) | Pablo Montero‐Manso, George A. Athanasopoulos, Rob J. Hyndman et al. | 313 | cites it |
| 2022-12-02 | [Forecast evaluation for data scientists: common pitfalls and best practices](https://doi.org/10.1007/s10618-022-00894-5) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Hansika Hewamalage, Klaus Ackermann, Christoph Bergmeir | 296 | cites it |
| 2020-05-28 | [Retail sales forecasting with meta-learning](https://doi.org/10.1016/j.ejor.2020.05.038) | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Shaohui Ma, Robert A. Fildes | 180 | cites it |
| 2022-03-31 | [Novel features for time series analysis: a complex networks approach](https://doi.org/10.1007/s10618-022-00826-3) | [Data Mining and Knowledge Discovery](https://scholar4ai.com/journals/data-mining-and-knowledge-discovery.md) | Vanessa Freitas Silva, Maria Eduarda Silva, Pedro Ribeiro et al. | 29 | cites it |
| 2021-10-19 | [Forecast with forecasts: Diversity matters](https://doi.org/10.1016/j.ejor.2021.10.024) | [European Journal of Operational Research](https://scholar4ai.com/journals/european-journal-of-operational-research.md) | Yanfei Kang, Wei Cao, Fotios Petropoulos et al. | 5 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). tsfeatures (R package): papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/cran-tsfeatures

```bibtex
@misc{scholar4ai-code-cran-tsfeatures,
  author = {{Scholar4AI}},
  title = {tsfeatures (R package): papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/cran-tsfeatures}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).